The Pandemic Response Commons.

Objectives: A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. Methods: The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC. Results: The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Discussion and conclusions: Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data.

NCI Cancer Research Data Commons: Resources to Share Key Cancer Data.

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404.

NCI Cancer Research Data Commons: Core Standards and Services.

The NCI Cancer Research Data Commons (CRDC) is a collection of data commons, analysis platforms, and tools that make existing cancer data more findable and accessible by the cancer research community. In practice, the two biggest hurdles to finding and using data for discovery are the wide variety of models and ontologies used to describe data, and the dispersed storage of that data. Here, we outline core CRDC services to aggregate descriptive information from multiple studies for findability via a single interface and to provide a single access method that spans multiple data commons. See related articles by Wang et al., p. 1388, Pot et al., p. 1396, and Kim et al., p. 1404.

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and Methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.

Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots.

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

Prostate cancer patient stratification by molecular signatures in the Veterans Precision Oncology Data Commons.

Veterans are at an increased risk for prostate cancer, a disease with extraordinary clinical and molecular heterogeneity, compared with the general population. However, little is known about the underlying molecular heterogeneity within the veteran population and its impact on patient management and treatment. Using clinical and targeted tumor sequencing data from the National Veterans Affairs health system, we conducted a retrospective cohort study on 45 patients with advanced prostate cancer in the Veterans Precision Oncology Data Commons (VPODC), most of whom were metastatic castration-resistant. We characterized the mutational burden in this cohort and conducted unsupervised clustering analysis to stratify patients by molecular alterations. Veterans with prostate cancer exhibited a mutational landscape broadly similar to prior studies, including KMT2A and NOTCH1 mutations associated with neuroendocrine prostate cancer phenotype, previously reported to be enriched in veterans. We also identified several potential novel mutations in PTEN, MSH6, VHL, SMO, and ABL1 Hierarchical clustering analysis revealed two subgroups containing therapeutically targetable molecular features with novel mutational signatures distinct from those reported in the Catalogue of Somatic Mutations in Cancer database. The clustering approach presented in this study can potentially be used to clinically stratify patients based on their distinct mutational profiles and identify actionable somatic mutations for precision oncology.

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning.

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies.

Optimizing Contrast Resolution in Digital Chest Radiography by Varying Copper Filtration and kVp.

PURPOSE: To measure the effect of increasing kilovoltage peak (kVp) and copper filtration thickness on entrance skin exposure and contrast resolution for chest radiography performed using digital flat-panel detectors. METHODS: A phantom-based experiment was conducted in which 24 radiographs of a quality control chest phantom were obtained at varying kVp levels and copper filtration thicknesses. The entrance skin exposure was measured and analyzed for each exposure. All radiographs were analyzed based on measured pixel values and contrast:noise ratio (CNR) and using subjective analysis, which focused on contrast resolution assessment performed by 4 radiologists. RESULTS: The results from the subjective image analysis showed that increasing copper filtration in increments of 0.1 mm resulted in less of a decrease in contrast resolution compared with increasing the kVp by 10 kVp, and that contrast resolution is more dependent on energy level than on filtration. The results from objective image analysis indicated that CNR decreased when kVp increased at all filtration thicknesses, but consistent dependency between CNR and filtration was not evident. Exposure data analysis showed an average 46% decrease in entrance skin exposure for each increase of 0.1 mm in copper filtration thickness. DISCUSSION: Although subjective and objective data analysis results indicated that increases of copper filtration are more beneficial to maintaining contrast resolution and reducing entrance skin exposure compared with increases of kVp, objective image data analysis showed a greater reduction in contrast resolution when kVp is increased. These results validate previous research that concluded that copper filtration should be considered as a dose-reduction and image-optimization strategy in digital radiography departments. CONCLUSION: Although entrance skin exposure reduction can be accomplished using higher kVp and copper filtration, increasing copper filtration thickness could be considered to minimize the loss of contrast resolution for routine chest imaging when digital flat-panel detectors are used.

Structure Revision of Type B Polycyclic Polyprenylated Acylphloroglucinols Fused to a Partly Reduced Furan Ring.

Four previous papers reported the isolation and structural determination of 10 polycyclic polyprenylated acylphloroglucinols (PPAPs), uraliones F, G, K, and O, attenuatumiones E and F, and scabrumiones A-D, from Hypericum species. Their structures were identified as type B PPAPs that featured not only the characteristic acyl group at C-3 of the bicyclo[3.3.1]nonane core but also a partly reduced furan ring fused to the C-1-C-2-O-2 atoms of the core. However, the 1D and 2D NMR data of these compounds were more consistent with type A PPAPs that featured not only the acyl group at C-1 but also a partially reduced furan ring fused to the C-3-C-2-O-2 atoms of the core. Now we revise these 10 previously proposed structures to the corresponding type A PPAPs via NMR analysis. Additionally, we propose a rule that uses NMR data to determine whether a particular PPAP that is fused to a partly reduced furan ring at C-3-C-2-O-2 or C-1-C-2-O-2 is type A or type B, respectively. We also propose a rule to assign the relative configurations of corresponding type A PPAPs at C-18 and revise the configurations of sampsonione N, hypericumoxides A-C, and hyperscabin G.

A Conformationally Restricted Gold(III) Complex Elicits Antiproliferative Activity in Cancer Cells.

Diamine ligands are effective structural scaffolds for tuning the reactivity of transition-metal complexes for catalytic, materials, and phosphorescent applications and have been leveraged for biological use. In this work, we report the synthesis and characterization of a novel class of cyclometalated [C^N] Au(III) complexes bearing secondary diamines including a norbornane backbone, (2R,3S)-N2,N3-dibenzylbicyclo[2.2.1]heptane-2,3-diamine, or a cyclohexane backbone, (1R,2R)-N1,N2-dibenzylcyclohexane-1,2-diamine. X-ray crystallography confirms the square-planar geometry and chirality at nitrogen. The electronic character of the conformationally restricted norbornane backbone influences the electrochemical behavior with redox potentials of -0.8 to -1.1 V, atypical for Au(III) complexes. These compounds demonstrate promising anticancer activity, particularly, complex 1, which bears a benzylpyridine organogold framework, and supported by the bicyclic conformationally restricted diaminonorbornane, shows good potency in A2780 cells. We further show that a cellular response to 1 evokes reactive oxygen species (ROS) production and does not induce mitochondrial dysfunction. This class of complexes provides significant stability and reactivity for different applications in protein modification, catalysis, and therapeutics.

Mortality in ASIA Impairment Scale grade A to D Patients With Odontoid Fracture and Magnetic Resonance Imaging Evidence of Spinal Cord Injury.

Odontoid fractures are common, often presenting in the elderly after a fall and infrequently associated with traumatic spinal cord injury (tSCI). The goal of this study was to analyze predictors of mortality and neurological outcome when odontoid fractures were associated with signal change on magnetic resonance imaging (MRI) at admission. Over an 18-year period (2001-2019), 33 patients with odontoid fractures and documented tSCI on MRI were identified. Mean age was 65.3 years (standard deviation [SD] = 17.2), and 21 patients were male. The mechanism of injury was falls in 25 patients, motor vehicle accidents in 5, and other causes in 3. Mean Injury Severity Score (ISS) was 40.5 (SD = 30.2), Glasgow Coma Scale (GCS) score was 13 (SD = 3.4), and American Spinal Injury Association (ASIA) motor score (AMS) was 51.6 (SD = 42.7). ASIA Impairment Scale (AIS) grade was A, B, C, and D in 9, 2, 3, and 19 patients, respectively. Mean intramedullary lesion length was 32.3 mm (SD = 18.6). The odontoid peg was displaced ventral or dorsal in 15 patients. Twenty patients had surgical intervention: anterior odontoid screw fixation in 7 and posterior spinal fusion in 13. Eleven (33.3%) patients died in this series: withdrawal of medical care in 5; anoxic brain injury in 4; and failure of critical care management in 2. Univariate logistic regression indicated that GCS score (p < 0.014), AMS (p < 0.002), AIS grade (p < 0.002), and ISS (p < 0.009) were risk factors for mortality. Multi-variate regression analysis indicated that only AMS (p < 0.002) had a significant relationship with mortality when odontoid fracture was associated with tSCI (odds ratio, 0.963; 95% confidence interval, 0.941-0.986).

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Deep learning generates synthetic cancer histology for explainability and education.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Total synthesis and structural reassignment of garcinielliptone FC, a polycyclic polyprenylated acylphloroglucinol with diverse bioactivity.

Garcinielliptone FC (GFC) was assigned to be a type A polycyclic polyprenylated acylphloroglucinol (PPAP) and was found to exhibit diverse biological activities. Now we revise the structure of GFC to xanthochymol, a type B PPAP, via NMR and total synthesis methods. The total syntheses of (±)-xanthochymol and (±)-cycloxanthochymol were accomplished in 12 and 13 steps, respectively.

Riluzole in Spinal Cord Injury Study (RISCIS)-Pharmacokinetic (PK) Sub-Study: An Analysis of Pharmacokinetics, Pharmacodynamics, and Impact on Axonal Degradation of Riluzole in Patients With Traumatic Cervical Spinal Cord Injury Enrolled in the RISCIS Phase III Randomized Controlled Trial.

To date, no drug therapy has shown significant efficacy in improving functional outcomes in patients with acute spinal cord injury (SCI). Riluzole is an approved benzothiazole sodium channel blocker to attenuate neurodegeneration in amyotrophic lateral sclerosis (ALS) and is of interest for neuroprotection in SCI. In a Phase I clinical trial (ClinicalTrials.gov Identifier: NCT00876889), riluzole was well tolerated with a 2-week treatment at the dose level approved for ALS and exhibited potential efficacy in patients with SCI. The acute and progressive nature of traumatic SCI and the complexity of secondary injury processes alter the pharmacokinetics (PK) of therapeutics. In the PK sub-study of the multi-center, randomized, placebo-controlled, double-blinded Riluzole in Spinal Cord Injury Study (RISCIS) Phase II/III trial (ClinicalTrials.gov Identifier: NCT01597518), a total of 32 SCI patients were enrolled, and most of our patients were middle-age Caucasian males with head and neck injuries. We studied the PK and pharmacodynamics (PD) of riluzole on motor recovery, measured by International Standards for Neurological Classification of SCI (ISNCSCI) Motor Score at injury and at 3-month and 6-month follow-ups, along with levels of the axonal injury biomarker phosphorylated neurofilament heavy chain (pNF-H), during the 2-week treatment. PK modeling, PK/PD correlations were developed to identify the potential effective exposure of riluzole for intended PD outcomes. The longitudinal impacts of SCI on the PK of riluzole are characterized. A time-varying population PK model of riluzole is established, incorporating time-varying clearance and volume of distribution from combined data of Phase I and Phase II/III trials. With the developed model, a rational, optimal dosing scheme can be designed with time-dependent modification to preserve the required therapeutic exposure of riluzole. The PD of riluzole and the relationship between PK and neurological outcomes of the treatment were established. The time course of efficacy in total motor score improvement (ΔTMS) and pNF-H were monitored. A three-dimensional (3D) PK/PD correlation was established for ΔTMS at 6 months with overall riluzole exposure area under the curve for Day 0-Day14 (AUCD0-D14) and baseline TMS for individual patients. Patients with baseline TMS between 1 and 36 benefited from the optimal exposure range of 16-48 mg*h/mL. The PD models of pNF-H revealed the riluzole efficacy, as treated subjects exhibited a diminished increase in progression of pNF-H, indicative of reduced axonal breakdown. The independent parameter of area between effective curves (ABEC) between the time profiles of pNF-H in placebo and treatment groups was statistically identified as a significant predictor for the treatment effect on the biomarker. A mechanistic clinical outcomes (CO)/PD (pNF-H) model was established, and the proposed structure demonstrated the feasibility of PK/PD/CO correlation model. No appreciable hepatic toxicity was observed with the current riluzole treatment regimen. The development of effective treatment for SCI is challenging. However, the future model-informed and PK-guided drug development and regimen modification can be rationally executed with the optimal dosing regimen design based on the developed 3D PK/PD model. The PK/PD/CO model can serve as a rational guide for future drug development, PKPD model refinement, and extension to other studies in SCI settings.

North American Clinical Trials Network for Spinal Cord Injury Registry: Methodology and Analysis.

The North American Clinical Trials Network (NACTN) for Spinal Cord Injury (SCI) is a consortium of neurosurgery departments at university affiliated hospitals with medical, nursing, and rehabilitation personnel who are skilled in the assessment, evaluation, and management of SCI. NACTN was established with the goal of consistently advancing the quality of life of people with SCI through clinical trials of new therapies that provide robust evidence of safety and effectiveness. A prospective multi-center Registry was created to collect the natural course of the acute traumatic SCI patient from time of injury to 12 months follow-up. NACTN's network of hospitals enrolls a significant number of patients, defines and adheres to standard protocols, and provides the infrastructure and highly skilled personnel to conduct trials of therapy for SCI. Registry data have been used by academic institutions and by the biotechnology and pharmaceutical sectors to create comparison datasets for Phase I clinical trials of new therapies.

Variability in Early Surgery for Acute Cervical Spinal Cord Injury Patients: An Opportunity for Enhanced Care Delivery.

Data supporting the benefits of early surgical intervention in acute spinal cord injury (SCI) is growing. For early surgery to be accomplished, understanding the causes of variabilities that effect the timing of surgery is needed to achieve this goal. The purpose of this analysis is to determine factors that affect the timing of surgery for acute cervical SCI within the North American Clinical Trials Network (NACTN) for SCI registry. Patients in the NACTN SCI registry from 2005 to 2019 with a cervical SCI, excluding acute traumatic central cord syndrome, were analyzed for time elapsed from injury to arrival to the hospital, and time to surgery. Two categories were defined: 1) Early Arrival with Early Surgery (EAES) commenced within 24 h of injury, and 2) Early Arrival but Delayed Surgery (EADS), with surgery occurring between 24 to 72 h post-injury. Patients' demographic features, initial clinical evaluation, medical comorbidities, neurological status, surgical intervention, complications, and outcome data were correlated with respect to the two arrival groups. Of the 222 acute cervical SCI patients undergoing surgery, 163 (73.4%) were EAES, and 59 (26.6%) were EADS. There was no statistical difference in arrival time between the EAES and EADS groups. There was a statistical difference in the median arrival time to surgery between the EAES group (9 h) compared with the EADS group (31 h; p < 0.05). There was no statistical difference in race, sex, age, mechanism of injury, Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II scores, or medical comorbidities between the two groups, but the EAES group did present with a significantly lower systolic blood pressure (p < 0.05). EADS patients were more likely to present as an American Spinal Injury Association Impairment Scale (AIS) D than EAES (p < 0.05). Early surgery was statistically more likely to occur if the injury occurred over the weekend (p < 0.05). There were variations in the rates of early surgery between the eight NACTN sites within the study, ranging from 57 to 100%. Of the 114 patients with 6-month outcome data, there was no significant change between the two groups regarding AIS grade change and motor/pin prick/light touch score recovery. A trend towards improved motor scores with early surgery was not statistically significant (p = 0.21). Although there is data that surgery within 24 h of injury improves outcomes and can be performed safely, there remain variations in care outside of clinical trials. In the present study of cervical SCI, NACTN achieved its goal of early surgery in 73.4% of patients from 2005-2019 who arrived within 24 h of their injury. Variability in achieving this goal was related to severity of neurological injury, the day of the week, and the treating NACTN center. Evaluating variations within our network improves understanding of potential systemic limitations and our decision-making process to accomplish the goal of early surgery.

Lived experience-centred word clouds may improve research uncertainty gathering in priority setting partnerships.

INTRODUCTION: AO Spine RECODE-DCM was a multi-stakeholder priority setting partnership (PSP) to define the top ten research priorities for degenerative cervical myelopathy (DCM). Priorities were generated and iteratively refined using a series of surveys administered to surgeons, other healthcare professionals (oHCP) and people with DCM (PwDCM). The aim of this work was to utilise word clouds to enable the perspectives of people with the condition to be heard earlier in the PSP process than is traditionally the case. The objective was to evaluate the added value of word clouds in the process of defining research uncertainties in National Institute for Health Research (NIHR) James Lind Alliance (JLA) Priority Setting Partnerships. METHODS: Patient-generated word clouds were created for the four survey subsections of the AO Spine RECODE-DCM PSP: diagnosis, treatment, long-term management and other issues. These were then evaluated as a nested methodological study. Word-clouds were created and iteratively refined by an online support group of people with DCM, before being curated by the RECODE-DCM management committee and expert healthcare professional representatives. The final word clouds were embedded within the surveys administered at random to 50% of participants. DCM research uncertainties suggested by participants were compared pre- and post-word cloud presentation. RESULTS: A total of 215 (50.9%) participants were randomised to the word cloud stream, including 118 (55%) spinal surgeons, 52 (24%) PwDCM and 45 (21%) oHCP. Participants submitted 434 additional uncertainties after word cloud review: word count was lower and more uniform across each survey subsections compared to pre-word cloud uncertainties. Twenty-three (32%) of the final 74 PSP summary questions did not have a post-word cloud contribution and no summary question was formed exclusively on post-word cloud uncertainties. There were differences in mapping of pre- and post-word cloud uncertainties to summary questions, with greater mapping of post-word cloud uncertainties to the number 1 research question priority: raising awareness. Five of the final summary questions were more likely to map to the research uncertainties suggested by participants after having reviewed the word clouds. CONCLUSIONS: Word clouds may increase the perspective of underrepresented stakeholders in the research question gathering stage of priority setting partnerships. This may help steer the process towards research questions that are of highest priority for people with the condition.